import os
import argparse
import torch
import json

from maplm.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from maplm.conversation import conv_templates, SeparatorStyle
from maplm.model.builder import load_pretrained_model
from maplm.utils import disable_torch_init
from maplm.mm_utils import tokenizer_maplm_token, get_model_name_from_path, KeywordsStoppingCriteria

from PIL import Image

import requests
from PIL import Image
from io import BytesIO
from transformers import TextStreamer


def load_image(image_file):
    if image_file.startswith('http') or image_file.startswith('https'):
        response = requests.get(image_file)
        image = Image.open(BytesIO(response.content)).convert('RGB')
    else:
        image = Image.open(image_file).convert('RGB')
    return image


def main(args):
    # Model
    disable_torch_init()

    model_name = get_model_name_from_path(args.model_path)
    tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name)
    # tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, args.load_8bit, args.load_4bit)

    if 'llama-2' in model_name.lower():
        conv_mode = "llava_llama_2"
    elif "v1" in model_name.lower():
        conv_mode = "llava_v1"
    elif "mpt" in model_name.lower():
        conv_mode = "mpt"
    else:
        conv_mode = "llava_v0"

    if args.conv_mode is not None and conv_mode != args.conv_mode:
        print(
            '[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode,
                                                                                                              args.conv_mode,
                                                                                                              args.conv_mode))
    else:
        args.conv_mode = conv_mode

    with open(args.json_file, 'r') as source_file:
        input_data = json.load(source_file)

    count = 0
    for data_item in input_data:
        conv = conv_templates[args.conv_mode].copy()
        if "mpt" in model_name.lower():
            roles = ('user', 'assistant')
        else:
            roles = conv.roles

        image_file = os.path.join(args.image_folder, data_item["image"])
        image = load_image(image_file)
        image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].half().cuda()
        image_tensor = None

        pointcloud_file = os.path.join(args.image_folder, data_item["pointcloud"])
        pointcloud = load_image(pointcloud_file)
        pointcloud_tensor = image_processor.preprocess(pointcloud, return_tensors='pt')['pixel_values'].half().cuda()
        # pointcloud_tensor = None
        
        conversations = data_item["conversations"]

        for i in range(0, len(conversations), 2):
            if image is not None:
                # first message
                inp = f"{roles[0]}: Assume you are monitoring a driving car. What are these images from sensors?"
                if model.config.mm_use_im_start_end:
                    inp = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + inp
                else:
                    inp = DEFAULT_IMAGE_TOKEN + '\n' + inp
                conv.append_message(conv.roles[0], inp)
                image = None
            else:
                # later messages
                inp = f"{roles[0]}: " + conversations[i]["value"]
                conv.append_message(conv.roles[0], inp)
            conv.append_message(conv.roles[1], None)

            prompt = conv.get_prompt()

            # print(prompt)

            input_ids = tokenizer_maplm_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(
                0).cuda()
            stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
            keywords = [stop_str]
            stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
            streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

            with torch.inference_mode():
                output_ids = model.generate(
                    input_ids,
                    images=image_tensor,
                    pointclouds=pointcloud_tensor,
                    do_sample=True,
                    temperature=0.2,
                    max_new_tokens=1024,
                    streamer=streamer,
                    use_cache=True,
                    stopping_criteria=[stopping_criteria])

            outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
            conv.messages[-1][-1] = outputs
            conversations[i + 1]["value"] = outputs

        conv = None
        count += 1

    with open('output_pointcloud_only.json', 'w') as destination_file:
        json.dump(input_data, destination_file, indent=4)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
    parser.add_argument("--model-base", type=str, default=None)
    parser.add_argument("--json-file", type=str, required=True)
    parser.add_argument("--image-folder", type=str, required=True)
    parser.add_argument("--num-gpus", type=int, default=1)
    parser.add_argument("--conv-mode", type=str, default=None)
    parser.add_argument("--temperature", type=float, default=0.2)
    parser.add_argument("--max-new-tokens", type=int, default=512)
    parser.add_argument("--load-8bit", action="store_true")
    parser.add_argument("--load-4bit", action="store_true")
    parser.add_argument("--debug", action="store_true")
    args = parser.parse_args()
    main(args)
